A customer asked:
They want to do a DOE with a small chromo column (1.6cm diameter). They have already some historic data from a Lab-scale chromo column (5cm diameter). Can we include these results in the DOE to get better model results? Especially how to tell the design that the software understands that there have been used different scales? Using blocks or just as categorical factor? Even better would be to get confirmed that the chromo column does not have any effect. If it has, what it should tell us what offset we can use for scaling. (important for our scale down model qualification
(I exclude the option to use a blocking variable in the design itself, because we cannot run additional 5cm experiments)
My suggestion would be to use an additional blocking factor for scaling size 5cm and 1.6cm, therefore the model needs to be adjusted with a random effect for the blocking factor. If we use just the categorical scaling size factor, we may not get information about the desired scaling offset, as I just have the different groups but no information about the size effect itself.
Do you have any experiences on that or opinions on what's best to approach this? Could it be an issue (adding bias) when they use way more historic data than the small size experimental data.
Thanks!
